The present invention generally relates to machine learning, particularly with product configuration engines for electronic data processing.
Processing a transaction generally involves the movement of assets. In some respects, the movement of the assets may vary based on the entities involved, the one or more types of transactions required by the movement, and/or the various instruments available, among other potential attributes. Further, the movement may be characterized based on the sources and/or sinks of the movement, various regional regulations, multiple currencies that may be involved in the movement, and/or the types of assets such as the products associated with the movement, among other attributes as well. In some instances, numerous attributes may be determined to qualify the movement internally and externally from the perspective of a participating entity involved in the transaction.
In various circumstances, the number of records maintained by hardware processors, memories, and/or data storage components may proportionally increase to thousands, millions, and/or possibly billions of fields, potentially based on the increasing number of attributes described above. Thus, in some instances, numerous fields may be associated with a simple transaction such that, for example, each of the participating entities involved may provide their respective approvals. Further, the numerous fields clustered with the transaction may create a number of system inefficiencies. Thus, as demonstrated in the scenarios above, there may be various inefficiencies associated with systems that handle larger volumes of fields associated with transactions. Further, it may be required to reduce and/or eliminate the latency involved with processing the transactions based on user experience requirements, service level agreements, and/or market demands and costs, among other possible factors.
As such, there is much need for technological advancements in various aspects of computer technology in the realm of computer networks and particularly with systems associated with transactions to optimize the management of data amongst the participating entities to improve system performance and efficiency.
Embodiments of the present disclosure and their advantages may be understood by referring to the detailed description herein. It should be appreciated that reference numerals may be used to illustrate various elements and features provided in the figures. The figures may illustrate various examples for purposes of illustration and explanation related to the embodiments of the present disclosure and not for purposes of any limitation.